There are a number of applications in which it may be desirable to detect a count of people over a particular area, such as space optimization, planning and maintenance, HVAC control, and data analytics driven marketing. For example, people count may be useful as one of the items of input data in a marketing analysis. Or for space optimization, a count of people in (pseudo) real time may be useful to identify temporal and spatial usage patterns. In another example, people sensors may be used in a lighting system to extract information relating to people in the area covered by the lighting system. More modern lighting systems can incorporate sensors into the lighting network, e.g. by incorporating a sensor in each luminaire, so as to allow the aggregation of sensor data from multiple sensors in the environment. Using suitable sensors, this allows the luminaires to share information on, say, occupancy, activity patterns, changes in temperature or humidity, daylight levels, etc.
Historically, such sensor mechanisms have been relatively unsophisticated. For example, combinations of timers and motion sensors have been used to selectively active luminaires in response to recently sensed movement in the environment. An example of such a motion sensor is a passive infra-red (“PIR”) motion sensor, which uses infrared radiation emitted from moving bodies to detect their motion. More recently, people counting techniques have used the aggregation of sensor data from individual image capture devices (or “vision sensors”) to generate a count of people in the area covered by their field of view. For instance, such a system may be part of a connected lighting system with a vision sensor in each luminaire, or a vision sensor associated to a group of luminaires. A vision sensor, optimized for cost and complexity, can provide much richer data than a conventional PIR sensor that is commonly used in lighting systems.
A number of other potential problems may occur with an image sensing unit. For instance, the sensor unit may develop an internal fault (e.g. failure of the sensor element, or malware), or the sensor unit may be subject to unauthorized or unskilled modification. Such problems may lead to the performance of the sensor being compromised, i.e. an incorrect number of people are reported. Such situations may in turn lead to application errors or misbehaviour, e.g. errors in estimated number of people at a higher system level.
U.S. Pat. No. 8,648,908 recognizes that sensor errors can occur in visitor counting systems, and that this can result in an incorrect people count. To address this, this document teaches that the results from the sensor are tagged with a time stamp or sequential record number. When the results are then examined, a missing timestamp or counter values is taken as indicative of a faulty sensor.